Existing techniques for providing recommendations for goods and services to users over the Internet or other communication networks are deficient in that such techniques are subject to fraudulent manipulation, can require a large amount of overhead, and often fail to consider sufficiently wide range of sources of the goods and services.
These existing techniques include, by way of example, on-line vendor rating services implemented by public service organizations such as the Better Business Bureau, buyer and seller ratings available via auction web sites such as eBay, and customer reviews on merchandise web sites such as Amazon. Fraudulent manipulation in these and other techniques can arise when users submit input such as complaints, ratings, reviews, etc. that are incorrect, biased or otherwise misrepresented. Excessive overhead will generally result if a web site or other entity that generates the recommendations is required to verify the accuracy of the above-noted user inputs, and such overhead increases dramatically with the number of sources considered.
It is therefore apparent that for these conventional techniques there is a direct relationship between the cost of producing a recommendation, and the accuracy and timeliness of that recommendation.
A related difficulty is that the conventional techniques, in order to produce a sufficiently accurate and timely recommendation, may require an excessive amount of computational resources, and therefore may not be readily implemented in electronic commerce applications involving mobile telephones, personal digital assistants (PDAs), hand-held computers or other mobile information processing devices.
A need therefore exists for improved techniques for generating secure recommendations over the Internet or other communication networks. Such techniques should preferably utilize minimal amounts of computational resources and other overhead, and should be adaptable for efficient implementation in conjunction with mobile information processing devices.